Chemical production data adjustment method, device, apparatus, and computer readable medium

Abstract

This disclosure provides embodiments of a method, apparatus, equipment, and computer-readable medium for adjusting chemical production data. One specific implementation of the method includes: acquiring a historical chemical data sequence; filtering each historical chemical data point in the historical chemical data sequence to generate a filtered chemical data sequence; performing simulation testing on the filtered chemical data sequence to generate a simulation test data sequence; generating target chemical parameter information based on the simulation test data sequence; and performing chemical data adjustment operations based on the target chemical parameter information. This implementation can improve the yield and quality of chemical products.

Description

Technical Field

[0001] The embodiments of this disclosure relate to the field of computer technology, and more specifically to methods, apparatus, devices, and computer-readable media for adjusting chemical production data. Background Technology

[0002] Chemical production data adjustment is a technique used to dynamically adjust the input of materials and the load data of equipment in the chemical production process. Currently, the common method for adjusting chemical data is to manually adjust the materials and equipment load data required for chemical production within a certain range based on experience.

[0003] However, the inventors discovered that when adjusting chemical data using the above method, the following technical problems often arise:

[0004] First, manual adjustments are time-consuming and lack accuracy in controlling material usage. This not only easily leads to material waste but also increases equipment wear and tear, resulting in a decrease in the yield and quality of the chemical products produced.

[0005] Secondly, if each chemical data point is optimized and adjusted individually, the interaction between chemical data and equipment load cannot be fully considered, leading to a decrease in the output of chemical products. If all chemical data points are optimized and adjusted at the same time, the data at different times in the chemical production process cannot be accurately adjusted, and it takes a long time, resulting in a decrease in chemical production efficiency / output.

[0006] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0008] Some embodiments of this disclosure provide methods, apparatus, equipment, and computer-readable media for adjusting chemical production data to address one or more of the technical problems mentioned in the background section above.

[0009] In a first aspect, some embodiments of this disclosure provide a method for adjusting chemical production data. The method includes: acquiring a historical chemical data sequence; filtering each historical chemical data in the historical chemical data sequence to generate a filtered chemical data sequence; performing simulation tests on the filtered chemical data sequence to generate a simulation test data sequence; generating target chemical parameter information based on the simulation test data sequence; and performing chemical data adjustment operations based on the target chemical parameter information.

[0010] Secondly, some embodiments of this disclosure provide a chemical production data adjustment device, which includes: an acquisition unit configured to acquire a historical chemical data sequence; a screening and processing unit configured to screen each historical chemical data in the historical chemical data sequence to generate a screened chemical data sequence; a simulation testing unit configured to perform simulation testing on the screened chemical data sequence to generate a simulation test data sequence; a generation unit configured to generate target chemical parameter information based on the simulation test data sequence; and a chemical data adjustment unit configured to perform chemical data adjustment operations based on the target chemical parameter information.

[0011] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0012] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0013] The above-described embodiments of this disclosure have the following beneficial effects: the chemical production data adjustment method of some embodiments of this disclosure can improve the yield and quality of the generated chemical products. Specifically, the reason for the decrease in the yield and quality of the generated chemical products is that manual adjustment is time-consuming and the accuracy of material control is insufficient, which not only easily leads to material waste but also increases equipment wear and tear. Based on this, the chemical production data adjustment method of some embodiments of this disclosure firstly acquires historical chemical data sequences. By mining and applying historical data, the optimal historical chemical production data can be extracted for data adjustment. Secondly, each historical chemical data in the above-described historical chemical data sequence is screened to generate a screened chemical data sequence. Next, the screened chemical data sequence is simulated to generate a simulation test data sequence. Through simulation testing, simulation test data corresponding to the screened chemical data in the current chemical production environment can be generated, i.e., the chemical production result. Then, based on the above-described simulation test data sequence, target chemical parameter information is generated. After determining the simulation test data corresponding to the screened chemical data in the current chemical production environment, it can be used to determine whether the screened chemical data is suitable for the current production environment. Thus, it can be better used to generate target chemical parameter information. Finally, based on the aforementioned target chemical parameter information, chemical data adjustment operations are performed. Because the target chemical parameter information is generated, it can be used to replace manual adjustments, modifying parameters such as material usage and equipment load in the chemical production process. This improves the accuracy of material usage and reduces wear and tear on production equipment, ultimately increasing the yield and quality of chemical products. Attached Figure Description

[0014] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0015] Figure 1 This is a flowchart of some embodiments of the chemical production data adjustment method according to this disclosure;

[0016] Figure 2 These are schematic diagrams of some embodiments of a chemical production data adjustment device according to this disclosure;

[0017] Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0018] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0019] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0020] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0021] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0022] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0023] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] Figure 1 A flow 100 illustrating some embodiments of a chemical production data adjustment method according to the present disclosure is shown. This chemical production data adjustment method includes the following steps:

[0025] Step 101: Obtain historical chemical data sequences.

[0026] In some embodiments, the entity executing the chemical production data adjustment method can retrieve historical chemical data sequences from a database via wired or wireless means. Data generated during each chemical production process can be stored in a pre-defined database. Therefore, historical data can be retrieved from the database. The historical chemical data sequences can be arranged chronologically. Each historical chemical data point can represent various data points from a complete chemical production process. For example, historical chemical data may include, but is not limited to, at least one of the following: raw material usage, equipment control parameters, equipment temperature, equipment feed rate, and data (mass, density, temperature) of the chemical products (e.g., sodium hydroxide, chlor-alkali).

[0027] As an example, historical chemical data could be: [[“A-tank inlet alkali flow rate”: “51.4 cubic meters per hour”], [“B-tank cathode liquid outlet temperature”: “86.7 degrees Celsius”], [“Production result sodium hydroxide”: “concentration 31.9%”]].

[0028] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future wireless connection methods.

[0029] Step 102: Filter each historical chemical data in the historical chemical data sequence to generate a filtered chemical data sequence.

[0030] In some embodiments, the aforementioned executing entity may filter each historical chemical data in the aforementioned historical chemical data sequence to generate a filtered chemical data sequence.

[0031] In some optional implementations of certain embodiments, each historical chemical data point in the aforementioned historical chemical data sequence may include a chemical material input ratio. Furthermore, the process by which the executing entity filters each historical chemical data point in the aforementioned historical chemical data sequence to generate a filtered chemical data sequence may include the following steps:

[0032] Historical chemical data with chemical material input ratios greater than a preset input ratio threshold, included in the aforementioned historical chemical data sequence, are identified as filtered chemical data, resulting in a filtered chemical data sequence. The chemical material input ratio can be used to characterize the output of chemical production. Here, historical chemical data indicating a chemical material input ratio greater than the preset input ratio threshold has reference value and can be used to adjust chemical production parameters.

[0033] Step 103: Perform simulation tests on the screened chemical data sequences to generate simulation test data sequences.

[0034] In some embodiments, the aforementioned execution entity may perform simulation tests on the screened chemical data sequence to generate a simulation test data sequence.

[0035] In some optional implementations of certain embodiments, each piece of filtered chemical data in the above-mentioned filtered chemical data sequence may include: raw material data, equipment load data, and process parameters. Furthermore, the execution entity performing simulation tests on the above-mentioned filtered chemical data sequence to generate a simulation test data sequence may include the following steps:

[0036] The raw material data, equipment load data, and process parameters of each filtered chemical data sequence are input into a pre-defined chemical simulation model to generate simulation test data, resulting in a simulation test data sequence. The raw material data can be the quantity of materials required for chemical production. The equipment load data can be the load on the chemical production equipment, such as temperature, humidity, and the speed at which materials are transported. The process parameters can be the dimensions of the items required for chemical production. The chemical simulation model can be a model pre-constructed based on chemical production conversion formulas. The simulation test data can be the chemical production results corresponding to each filtered chemical data.

[0037] Step 104: Generate target chemical parameters based on the simulation test data sequence.

[0038] In some embodiments, the execution entity may generate target chemical parameter information based on the simulation test data sequence.

[0039] In some optional implementations of certain embodiments, each simulation test data point in the above simulation test data sequence may include a test material production ratio. Furthermore, the execution entity generating target chemical parameter information based on the above simulation test data sequence may include the following steps:

[0040] The first step involves identifying the simulation test data in the aforementioned simulation test data sequence whose test material input ratio exceeds a preset input ratio threshold as target test parameter information, thus obtaining a target test parameter information sequence. Each target test information in this sequence includes: simulation raw material data, simulation equipment load data, and simulation process parameters, arranged chronologically. Here, the data in the simulation raw material data, simulation equipment load data, and simulation process parameters may also include time markers used during the chemical production process. Specifically, considering that the current production environment differs from the historical production environment (e.g., different equipment wear), further filtering removes data from the historical data that is inapplicable to the current chemical production environment. This improves the rationality of the data used for adjusting chemical production data.

[0041] In practice, chemical production is generally carried out in stages. Therefore, data from different periods of chemical production can correspond to different time period identifiers. Thus, the aforementioned time identifiers can include not only timestamps but also time stage identifiers for chemical production.

[0042] The second step involves generating independent parameter groups and an overall parameter group based on the simulated raw material data, simulated equipment load data, and simulated process parameters included in the aforementioned target test parameter information sequence. Independent parameters within the independent parameter group can be parameters in the chemical production process that have a relatively small impact on other data. Overall parameters, on the other hand, can be parameters that have a significant impact on other parameters.

[0043] Optionally, the aforementioned execution entity may generate independent parameter groups and an overall parameter group based on the simulated raw material data, simulated equipment load data, and simulated process parameters included in the aforementioned target test parameter information sequence. This may include the following steps:

[0044] Step one: Based on a preset chemical process timeline, the simulated raw material data, simulated equipment load data, and simulated process parameters included in each target test parameter information sequence are grouped to generate a time-segment chemical data sequence set. The chemical process timeline can be the timeline corresponding to the chemical production process, including the time corresponding to each data point from start to finish (e.g., raw material input, equipment status adjustment, etc.). Secondly, since chemical production can generally be divided into different stages, the data from different stages on the corresponding timeline can be grouped. Here, the simulated raw material data, simulated equipment load data, and simulated process parameters corresponding to the same time stage in each target test parameter information can be identified as time-segment chemical data, resulting in a time-segment chemical data sequence. In practice, considering that unified optimization adjustments between different production stages can easily lead to mutual interference, grouping is performed.

[0045] Step two involves performing independent component analysis (ICA) on the chemical data from each time period within the aforementioned chemical data series set to generate independent parameter sets and an overall parameter set. Specifically, a pre-defined ICA algorithm can be used to perform ICA on the chemical data from each time period within the aforementioned chemical data series set to identify independent parameters and overall parameters.

[0046] The third step involves standardizing the independent parameter data of each target test information in the aforementioned target test parameter information sequence, resulting in an independent parameter matrix. First, data corresponding to each independent parameter can be selected from the simulation raw material data, simulation equipment load data, and simulation process parameters included in the target test information sequence, resulting in an independent data sequence. Here, since the target test parameters originate from historical chemical data, the timestamps corresponding to the historical chemical data generation are used. Therefore, the independent data in the independent data sequence can be arranged in chronological order of data generation. Next, each independent data in each independent data sequence can be vectorized to obtain an independent parameter vector. Finally, the independent parameter vectors can be combined into an independent parameter matrix according to the chronological order of the timeline. If there is no corresponding independent parameter vector at a certain moment on the timeline, zeros can be added. Here, vectorization can involve normalizing the data and arranging it in chronological order of data generation.

[0047] The fourth step involves standardizing the overall parameter data of each target test information in the aforementioned target test parameter information sequence, resulting in an overall parameter matrix. First, data corresponding to each overall parameter can be selected from the simulation raw material data, simulation equipment load data, and simulation process parameters included in the target test parameter information sequence, thus obtaining an overall data sequence. Similarly, since the target test parameters originate from historical chemical data, the timestamps of the historical chemical data generation are used. Therefore, the overall data in the overall data sequence can also be arranged in chronological order of data generation. Then, each independent data in each overall data sequence can be vectorized to obtain an overall parameter vector. Finally, the overall parameter vectors can be combined into an overall parameter matrix according to the chronological order of the timeline. If a corresponding overall parameter vector is not found at a certain moment on the timeline, it can be padded with zeros. Here, vectorization can be achieved by arranging the overall data in chronological order of data generation.

[0048] The fifth step involves inputting the aforementioned independent parameter matrices and overall parameter matrices into a pre-defined chemical parameter adjustment model to generate target chemical parameter information. First, the independent parameter matrices can be input into the independent parameter mapping network of the chemical parameter adjustment model for feature mapping, resulting in an independent parameter mapping feature matrix. Then, the overall parameter matrix can be input into the overall parameter mapping network of the chemical parameter adjustment model for feature mapping, resulting in an overall parameter mapping feature matrix. Feature mapping maps the matrices to a high-dimensional space. Next, the independent parameter mapping feature matrices and overall parameter mapping feature matrices can be input into a feature interaction encoding network to generate high-dimensional parameter feature codes. Here, the feature interaction encoding network can include an attention mechanism and a positional encoding algorithm. The attention mechanism can perform feature interaction between the independent parameter mapping feature matrices and the overall parameter mapping feature matrix. The positional encoding algorithm ensures the temporal position of the features after the attention mechanism interaction. Furthermore, the attention mechanism can be a spatiotemporal attention mechanism that includes both spatial and temporal dimensions. In the temporal dimension, it ensures that the model's characteristics conform to the temporal characteristics of the chemical production timeline, so that the generated parameters also possess temporal characteristics. In the spatial dimension, the model can interactively learn the features of independent parameters and the overall parameters to improve the accuracy of the final generated target chemical parameter information. Finally, the high-order encoded features of the parameters can be input into the parameter feature decoding network included in the chemical parameter adjustment model to obtain the target chemical parameter information. Here, the target chemical parameter information can include various time points on the time axis and their corresponding parameters. These parameters can be the amount of materials required at different time points in the chemical production process, equipment parameters, etc. Furthermore, the generation process can include constraints on various parameters to avoid unreasonable data generation.

[0049] As an example, the positional encoding algorithm could be a Positional Encoding algorithm. The decoding network could be a cross-attention network.

[0050] The above content, as an inventive point of this disclosure, solves the second technical problem mentioned in the background art: "If optimization and adjustment are performed only for each piece of chemical data individually, the mutual influence between chemical data and equipment load cannot be fully considered, leading to a decrease in the output of chemical products. If optimization and adjustment are performed simultaneously for each piece of chemical data, the data at different times in the chemical production process cannot be accurately adjusted, and it consumes a long time, resulting in a decrease in chemical production efficiency / output." The factors leading to a decrease in chemical production efficiency / output are often as follows: If optimization and adjustment are performed only for each piece of chemical data individually, the mutual influence between chemical data and equipment load cannot be fully considered, leading to a decrease in the output of chemical products. If optimization and adjustment are performed simultaneously for each piece of chemical data, the data at different times in the chemical production process cannot be accurately adjusted, and it consumes a long time. Solving these factors can improve the efficiency and output of chemical production. To achieve this effect, firstly, by generating independent parameter groups and overall parameter groups, data that does not affect other parameters can be selected. This avoids feature confusion during extraction, causing inaccurate features. Then, through data standardization, the target test information can be transformed into a parameter matrix according to the time axis order, so that feature extraction can be performed. Finally, by introducing a chemical parameter model, target chemical parameter information can be generated. This model includes independent parameter mapping networks, a global parameter mapping network, a feature cross-coding network, and a parameter feature decoding network, allowing the simultaneous generation of target chemical parameter information using both independent and global parameter matrices. Furthermore, the inclusion of the feature cross-coding network, along with its attention mechanism and positional encoding algorithm, ensures not only feature interaction between the independent and global parameter mapping feature matrices but also guarantees the temporal position of features after the attention mechanism's interaction. This ensures that the generated target chemical parameter information retains its temporal sequence and accuracy, facilitating adjustments to chemical production data and ultimately improving the efficiency and output of chemical production.

[0051] Step 105: Based on the target chemical parameters, perform chemical data adjustment operations.

[0052] In some embodiments, the aforementioned executing entity may perform chemical data adjustment operations based on the aforementioned target chemical parameter information.

[0053] In some optional implementations of certain embodiments, the target chemical parameter information may include a time-based sequence of parameters to be adjusted. Furthermore, the execution entity, based on the target chemical parameter information, performs a chemical data adjustment operation, which may include the following steps:

[0054] In accordance with the time sequence of the time axis, the parameters to be adjusted in the above-mentioned parameter sequence are sent to the target chemical equipment in turn, so that the target chemical equipment can perform parameter adjustment and chemical production operation.

[0055] As an example, the parameters to be adjusted could be: [Time point: "1st minute", "A tank inlet alkali flow rate": "50.3 cubic meters per hour"], [Time point: "2nd minute", "B tank cathode liquid outlet temperature": "87.02 degrees Celsius"], etc.

[0056] Optionally, the aforementioned implementing entity may also perform the following steps:

[0057] The first step is to determine the completion of chemical production and obtain data on the results of chemical production.

[0058] As an example, chemical production result data could be: ["Production result: Sodium hydroxide": "Concentration 32.14%"]].

[0059] The second step involves combining the aforementioned target chemical parameter information and chemical production result data to generate historical chemical data for use in the next adjustment of chemical data. This combination can involve defining the target chemical parameter information and chemical production result data as historical chemical data.

[0060] Optionally, the aforementioned implementing entity can also send the target chemical parameter information to a display terminal for display. This display allows staff to see the target chemical parameter information, and if any exceedances are detected, timely chemical production can be initiated to prevent dangerous situations.

[0061] The above-described embodiments of this disclosure have the following beneficial effects: the chemical production data adjustment method of some embodiments of this disclosure can improve the yield and quality of the generated chemical products. Specifically, the reason for the decrease in the yield and quality of the generated chemical products is that manual adjustment is time-consuming and the accuracy of material control is insufficient, which not only easily leads to material waste but also increases equipment wear and tear. Based on this, the chemical production data adjustment method of some embodiments of this disclosure firstly acquires historical chemical data sequences. By mining and applying historical data, the optimal historical chemical production data can be extracted for data adjustment. Secondly, each historical chemical data in the above-described historical chemical data sequence is screened to generate a screened chemical data sequence. Next, the screened chemical data sequence is simulated to generate a simulation test data sequence. Through simulation testing, simulation test data corresponding to the screened chemical data in the current chemical production environment can be generated, i.e., the chemical production result. Then, based on the above-described simulation test data sequence, target chemical parameter information is generated. After determining the simulation test data corresponding to the screened chemical data in the current chemical production environment, it can be used to determine whether the screened chemical data is suitable for the current production environment. Thus, it can be better used to generate target chemical parameter information. Finally, based on the aforementioned target chemical parameter information, chemical data adjustment operations are performed. Because the target chemical parameter information is generated, it can be used to replace manual adjustments, modifying parameters such as material usage and equipment load in the chemical production process. This improves the accuracy of material usage and reduces wear and tear on production equipment, ultimately increasing the yield and quality of chemical products.

[0062] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a chemical production data adjustment device, which are similar to... Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0063] like Figure 2As shown, a chemical production data adjustment device 200 in some embodiments includes: an acquisition unit 201, a screening and processing unit 202, a simulation testing unit 203, a generation unit 204, and a chemical data adjustment unit 205. The acquisition unit 201 is configured to acquire historical chemical data sequences; the screening and processing unit 202 is configured to screen each historical chemical data in the historical chemical data sequence to generate a screened chemical data sequence; the simulation testing unit 203 is configured to perform simulation testing on the screened chemical data sequence to generate a simulation test data sequence; the generation unit 204 is configured to generate target chemical parameter information based on the simulation test data sequence; and the chemical data adjustment unit 205 is configured to perform chemical data adjustment operations based on the target chemical parameter information.

[0064] It is understandable that the units described in the device 200 are related to the reference. Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, and will not be repeated here.

[0065] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0066] like Figure 3 As shown, the electronic device 300 may include a processing unit 301 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0067] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0068] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0069] It should be noted that, in some embodiments of this disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0070] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0071] The aforementioned computer-readable medium may be included in the aforementioned device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: acquire a historical chemical data sequence; filter and process a unit configured to filter each historical chemical data in the historical chemical data sequence to generate a filtered chemical data sequence; perform simulation testing on the filtered chemical data sequence to generate a simulation test data sequence; generate a target chemical parameter information based on the simulation test data sequence; and perform a chemical data adjustment unit configured to perform a chemical data adjustment operation based on the target chemical parameter information.

[0072] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0073] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0074] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit, a screening unit, a simulation testing unit, a generation unit, and a chemical data adjustment unit. The names of these units do not necessarily limit the specific unit; for example, an acquisition unit may also be described as "a unit for acquiring historical chemical data sequences."

[0075] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0076] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for adjusting chemical production data, comprising: Obtain historical chemical data sequences; Each historical chemical data point in the historical chemical data sequence is filtered to generate a filtered chemical data sequence. The screened chemical data sequence is subjected to simulation testing to generate a simulation test data sequence; Based on the simulation test data sequence, target chemical parameters information is generated; Based on the target chemical parameters, perform chemical data adjustment operations; Each simulation test data in the simulation test data sequence includes the production ratio of the test material; The step of generating target chemical parameter information based on the simulation test data sequence includes: Simulation test data whose production ratio of test materials exceeds a preset production ratio threshold within the simulation test data sequence are identified as target test parameter information, resulting in a target test parameter information sequence. Each target test information in this sequence includes: simulated raw material data, simulated equipment load data, and simulated process parameters. These target test information items are arranged chronologically. The simulated raw material data, simulated equipment load data, and simulated process parameters also include time markers indicating their input during the chemical production process. These time markers include not only timestamps but also time stage markers from the chemical production process. Based on the simulated raw material data, simulated equipment load data, and simulated process parameters included in the target test parameter information sequence, independent parameter groups and overall parameter groups are generated. The independent parameters in the independent parameter groups are those that have a relatively small impact on other data during the chemical production process, while the overall parameters are those that have a significant impact on other parameters, including: Based on a preset chemical process timeline, the simulated raw material data, simulated equipment load data, and simulated process parameters included in each target test parameter information sequence are grouped and processed to generate a time-segment chemical data sequence set; Independent component analysis is performed on each time period chemical data in each time period chemical data sequence in the aforementioned time period chemical data sequence set to generate independent parameter sets and overall parameter sets; Based on the independent parameter set, the independent parameter data of each target test information in the target test parameter information sequence are standardized to obtain an independent parameter matrix. Specifically, data corresponding to each independent parameter are selected from the simulation raw material data, simulation equipment load data, and simulation process parameters included in each target test information in the target test parameter information sequence to obtain an independent data sequence. Each independent data in each independent data sequence is vectorized to obtain an independent parameter vector. Finally, the independent parameter vectors are combined according to the time order on the time axis to form an independent parameter matrix. Based on the overall parameter group, the overall parameter data of each target test information in the target test parameter information sequence is standardized to obtain an overall parameter matrix. Specifically, data corresponding to each overall parameter is selected from the simulation raw material data, simulation equipment load data, and simulation process parameters included in each target test information in the target test parameter information sequence to obtain an overall data sequence. Each independent data point in each overall data sequence is vectorized to obtain an overall parameter vector. Finally, the overall parameter vectors are combined according to the time order on the time axis to form the overall parameter matrix. The independent parameter matrix and the overall parameter matrix are input into a preset chemical parameter adjustment model to generate target chemical parameter information. Specifically, the independent parameter matrix is ​​input into the independent parameter mapping network of the chemical parameter adjustment model for feature mapping, resulting in an independent parameter mapping feature matrix. The overall parameter matrix is ​​input into the overall parameter mapping network of the chemical parameter adjustment model for feature mapping, resulting in an overall parameter mapping feature matrix. Feature mapping maps the matrices to a high-dimensional space. The independent parameter mapping feature matrix and the overall parameter mapping feature matrix are input into a feature interaction coding network to generate high-dimensional parameter feature codes. The feature interaction coding network includes an attention mechanism and a positional coding algorithm. The attention mechanism performs feature interaction on the independent parameter mapping feature matrix and the overall parameter mapping feature matrix, while the positional coding algorithm ensures the temporal position of the features after the attention mechanism interaction. The high-dimensional parameter code features are input into the parameter feature decoding network included in the chemical parameter adjustment model to obtain target chemical parameter information. The target chemical parameter information includes various time points on the time axis and their corresponding parameters. These parameters represent the amount of materials and equipment parameters required at different time points in the chemical production process.

2. The method according to claim 1, wherein, The method further includes: The target chemical parameter information is sent to the display terminal for display.

3. The method according to claim 1, wherein, Each historical chemical data point in the historical chemical data sequence includes the production ratio of chemical materials; as well as The step of filtering each historical chemical data point in the historical chemical data sequence to generate a filtered chemical data sequence includes: Historical chemical data in the historical chemical data sequence whose chemical material input ratio is greater than a preset input ratio threshold are identified as filtered chemical data, thus obtaining the filtered chemical data sequence.

4. The method according to claim 1, wherein, Each piece of screened chemical data in the sequence of screened chemical data includes: raw material data, equipment load data, and process parameters; and The step of performing simulation tests on the screened chemical data sequences to generate simulation test data sequences includes: The raw material data, equipment load data, and process parameters of each filtered chemical data in the filtered chemical data sequence are input into a preset chemical simulation model to generate simulation test data, thus obtaining a simulation test data sequence.

5. The method according to claim 1, wherein, The target chemical parameter information includes a time-based sequence of parameters to be adjusted. as well as The step of performing chemical data adjustment operations based on the target chemical parameter information includes: According to the time sequence of the time axis, the parameters to be adjusted in the sequence of parameters to be adjusted are sent to the target chemical equipment in turn, so that the target chemical equipment can perform parameter adjustment and chemical production operation.

6. The method according to claim 5, wherein, The method further includes: In response to confirming the completion of chemical production, acquire chemical production result data; The target chemical parameters and the chemical production results data are combined to generate historical chemical data for use in the next adjustment of chemical data.

7. A chemical production data adjustment device, comprising: The acquisition unit is configured to acquire historical chemical data sequences; The filtering processing unit is configured to filter each historical chemical data in the historical chemical data sequence to generate a filtered chemical data sequence. The simulation test unit is configured to perform simulation tests on the screened chemical data sequence to generate a simulation test data sequence; The generation unit is configured to generate target chemical parameter information based on the simulation test data sequence; The chemical data adjustment unit is configured to perform chemical data adjustment operations based on the target chemical parameter information; Each simulation test data in the simulation test data sequence includes the production ratio of the test material; The generation unit is further configured to: Simulation test data whose production ratio of test materials exceeds a preset production ratio threshold within the simulation test data sequence are identified as target test parameter information, resulting in a target test parameter information sequence. Each target test information in this sequence includes: simulated raw material data, simulated equipment load data, and simulated process parameters. These target test information items are arranged chronologically. The simulated raw material data, simulated equipment load data, and simulated process parameters also include time markers indicating their input during the chemical production process. These time markers include not only timestamps but also time stage markers from the chemical production process. Based on the simulated raw material data, simulated equipment load data, and simulated process parameters included in the target test parameter information sequence, independent parameter groups and overall parameter groups are generated. The independent parameters in the independent parameter groups are those that have a relatively small impact on other data during the chemical production process, while the overall parameters are those that have a significant impact on other parameters, including: Based on a preset chemical process timeline, the simulated raw material data, simulated equipment load data, and simulated process parameters included in each target test parameter information sequence are grouped and processed to generate a time-segment chemical data sequence set; Independent component analysis is performed on each time period chemical data in each time period chemical data sequence in the aforementioned time period chemical data sequence set to generate independent parameter sets and overall parameter sets; Based on the independent parameter set, the independent parameter data of each target test information in the target test parameter information sequence are standardized to obtain an independent parameter matrix. Specifically, data corresponding to each independent parameter are selected from the simulation raw material data, simulation equipment load data, and simulation process parameters included in each target test information in the target test parameter information sequence to obtain an independent data sequence. Each independent data in each independent data sequence is vectorized to obtain an independent parameter vector. Finally, the independent parameter vectors are combined according to the time order on the time axis to form an independent parameter matrix. Based on the overall parameter group, the overall parameter data of each target test information in the target test parameter information sequence is standardized to obtain an overall parameter matrix. Specifically, data corresponding to each overall parameter is selected from the simulation raw material data, simulation equipment load data, and simulation process parameters included in each target test information in the target test parameter information sequence to obtain an overall data sequence. Each independent data point in each overall data sequence is vectorized to obtain an overall parameter vector. Finally, the overall parameter vectors are combined according to the time order on the time axis to form the overall parameter matrix. The independent parameter matrix and the overall parameter matrix are input into a preset chemical parameter adjustment model to generate target chemical parameter information. Specifically, the independent parameter matrix is ​​input into the independent parameter mapping network of the chemical parameter adjustment model for feature mapping, resulting in an independent parameter mapping feature matrix. The overall parameter matrix is ​​input into the overall parameter mapping network of the chemical parameter adjustment model for feature mapping, resulting in an overall parameter mapping feature matrix. Feature mapping maps the matrices to a high-dimensional space. The independent parameter mapping feature matrix and the overall parameter mapping feature matrix are input into a feature interaction coding network to generate high-dimensional parameter feature codes. The feature interaction coding network includes an attention mechanism and a positional coding algorithm. The attention mechanism performs feature interaction on the independent parameter mapping feature matrix and the overall parameter mapping feature matrix, while the positional coding algorithm ensures the temporal position of the features after the attention mechanism interaction. The high-dimensional parameter code features are input into the parameter feature decoding network included in the chemical parameter adjustment model to obtain target chemical parameter information. The target chemical parameter information includes various time points on the time axis and their corresponding parameters. These parameters represent the amount of materials and equipment parameters required at different time points in the chemical production process.

8. An electronic device, comprising: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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